Utility Owners’ Approaches to Conversion Quality Control
Kelly Jean Fergusson
P.E., Ph.D. Independent Consultant 224 Fernando Ave Palo Alto, CA 94306
Abstract
Make the most of every data conversion dollar by performing cost-effective quality control (QC)
in-house. Build cultural acceptance of digital data by involving engineering staff in the QC
checking at appropriate steps in the conversion process, with minimum time commitments.
Target your quality control efforts on the types of errors that affect the results you’ll be seeking
from AM/FM/GIS analyses, Ensure the conversion vendor is performing to specifications.
Most utilities entering the AM/FM/GIS arena perform conversion of their traditional engineering
records to digital format. Conversion of utilities and infrastructure drawings and data occurs at
the beginning of system implementation, and typically is the most expensive and critical
component of creating a AM/FM/GIS system. Ironically, at this time of project initiation, utility
owners are generally new to the AM/FM/GIS field and are uncertain of cost and benefit trade-offs
in the conversion process. This paper gives utility owners a better understanding of the
categories of data errors, their potential consequences, and structured procedures and methods to
perform quality control checking of the conversion data. These principles are applicable to the
conversion of water, wastewater, stormwater, gas, electrical, street lights, communications, and
planimetric data.
Introduction
The consequences of errors generated during a data conversion project can range from life-threatening
accidents to minor annoyances. But with limited staff time and funds, how do utility
owners prioritize conversion quality control (QC) efforts and target error checking toward the
types of errors that will have the most serious consequences to the organization’s anticipated use
of GIS ?
To answer this question, I will start by introducing a typical multiple-utility conversion project to
give the reader a baseline context for the rest of my remarks. I then bring up some trade-offs
regarding in-house staffing levels for quality control checking. Next, I will briefly outline six
types of errors in GIS graphic and attribute data. The body of the paper will contain some
observations and theories regarding the priorities of various types of QC checks given varying
project objectives and circumstances. I will conclude with a discussion of the importance of well
thought-out and realistic conversion specifications to ensure a smooth conversion and a
minimum of QC effort. ,
This paper assumes a basic knowledge of conversion terminology and methods. For an
introduction to these, please refer to (Fergusson and Eitzel, 1995).
Conversion Project Scenario
A gas and electric utility is a typical customer of a conversion project. The utility’s management
has decided it wants to use advancing GIS technology to help achieve more competitive facility
management and better customer service. Historically, perhaps the gas company acquired the
electric company, and its mylar or linen engineering maps. Both sets of maps, gas and electric,
have probably continued to be manually updated in some fashion, though updating may have
suffered due to periodic staff and budget constraints. Perhaps the map sets are at different scales,
the gas at 1“ = 40’ and the electric at 1” = 100’. The gas maps may include street curbs, and the
electric maps may show property lines. They may cover different but overlapping territory.
How would you approach this conversion project? A tried and workable solution would be to
hire one or more vendors to perform the steps of surveying, aerial flight and photogrammetry,
stereodigitizing, and board digitizing. Graphic components would be created by a vendor
photogrammetrically capturing the visible curb lines, gas valve covers, and electrical
appurtenances with a stereodigitizing process. Property lines could be input using coordinate
geometry (COGO) techniques from records of survey and subdivision plats. Each of the map
sets would then be digitized. As each map set is digitized, the operator typically categorizes the
various map features so that the map becomes “layered” or “intelligent”, according to a pre-determined
data base schema. The operator also inputs attribute data such as pipe diameter or
conductor material from reading map annotation. This is a fair] y typical and straightforward
conversion project.
As the digitized graphics start to come into the utility organization’s hands, it is the responsibility
of the utility to quality control check the data to make sure it meets with the specifications of the
contract. This is equivalent to a having building inspection when you are buying a home. The
inspection protects the buyer from losses due to faulty workmanship and ensures the owner gets
what s/he pays for. Like a first-time home buyer, the utility owner is typically fairly naive
regarding what to check for. Unlike the home buyer, the utility may need to perform the
checking or inspection process itself.
QC Staffing Considerations
Quality control checking of converted utilities digital data can be performed by regular in-house
staff, temporary staff, a quality control consultant firm, or any combination thereof. The total
staffing hours and mix the utility decides on will depend on the scope of the conversion, the
competence of the conversion vendor(s), and the day-to-day job demands on in-house staff (in
addition to QC checking) during the acceptance time frame. 10% to 15% of the conversion
budget is a reasonable figure to budget for quality control.
Giving in-house personnel the responsibility for checking the new digital maps will build their
confidence in the correctness of the maps, and they will be more inclined to use the new digital
product as it becomes available rather than sticking to the old ways. However, engineers and
technicians that would hopefully be assigned these quality control tasks are often too busy to take
on this additional though temporary workload. As an alternative, I have had success with using
local college engineering students as interns to perform certain tasks, while only a few essential
tasks are performed by the more experienced permanent staff. Interns or temporary employees
can use the permanent staff as a resource when questions or ambiguities arise. As another
alternative, the entire quality control process can be out-sourced to a company specializing in this
area. In this case you gain the advantage of an expert audit, but lose the benefits of building
grass-roots confidence in the data in-house. In all cases, a person very familiar with the
conversion project specifications should define and coordinate the quality control process and
should be knowledgeable and experienced enough to resolve any issues that arise.
Types of Errors
There are six different categories of error on a conversion project. Each main category has
several sub-categories. Although only the briefest description of the main categories and some
examples are listed here, a complete and in-depth typology of these errors is given in (Fergusson
and Eitzel, 1995). The intent of this current paper is to build on the previous paper by analyzing
the categories with respect to the cost-and-consequence trade-offs inherent in identifying and
correcting errors of different types.
Completeness (Suftlciency)
A completeness (sufficiency) error has occurred when something should (according to the
specifications) exist in the converted digital data, but is missing. For example, a feature or a
piece of text annotation which exists on the source maps does not appear in the digital data.
Classitlcation
A classification error has occurred if a feature or attribute exists in the digital data but is
classified incorrectly. For example, a gas feature appears on the electrical drawing, a valve is
classified as a fitting, or an installation date is classified as an abandoned date attribute.
Position
Absolute position errors exist if digital map features are not located within expected tolerances
with respect to their true positions. A relative position error exists if features are dislocated with
respect to each other. For example, if the source document shows a conduit running four feet
from the curb, and the digital shows the same conduit running 10 feet from a curb, a relative
position error is present.
Attribute Value
This type of error exists when annotation text and/or attribute values misrepresent the true value.
For example, if digital attributes should (according to the specification) match attributes values
listed on the source document, an attribute value error has occurred if the digital attribute for a
pipe diameter is 3“, but it is listed as 2“ on the source.
